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op_teller.cc
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op_teller.cc
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/inference/tensorrt/op_teller.h"
#include <bitset>
#include "paddle/fluid/framework/block_desc.h"
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/framework/phi_utils.h"
#include "paddle/fluid/inference/tensorrt/dynamic_shape_infermeta_factory.h"
#include "paddle/phi/api/ext/op_meta_info.h"
#include "paddle/phi/core/compat/op_utils.h"
#include "paddle/phi/core/kernel_factory.h"
namespace paddle::framework {
class OpDesc;
} // namespace paddle::framework
namespace paddle::inference::tensorrt {
// Check if it is a dynamic shape. If it is a dynamic shape, return true;
// otherwise, return false
bool IsDynamicShapeOp(const framework::OpDesc& desc) {
VLOG(3) << "forbid_dynamic_op_enter_into_trt is open";
auto* block = desc.Block();
auto inputs = desc.Inputs();
for (auto iter : inputs) {
for (auto var_name : iter.second) {
if (block) {
auto* var_desc = block->FindVar(var_name);
const auto shape = var_desc->GetShape();
for (auto ele : shape) {
if (ele < 0) {
return true;
}
}
}
}
}
auto outputs = desc.Outputs();
for (auto iter : outputs) {
for (auto var_name : iter.second) {
if (block) {
auto* var_desc = block->FindVar(var_name);
const auto shape = var_desc->GetShape();
for (auto ele : shape) {
if (ele < 0) {
return true;
}
}
}
}
}
return false;
}
// Just tell by the op_types.
struct SimpleOpTypeSetTeller : public Teller {
SimpleOpTypeSetTeller() { // NOLINT
#if IS_TRT_VERSION_GE(7130)
// use TensorRT plugin
teller_set.insert("group_norm");
teller_set.insert("multiclass_nms3");
teller_set.insert("multiclass_nms");
int8_teller_set.insert("multiclass_nms3");
int8_teller_set.insert("multiclass_nms");
#endif
#if IS_TRT_VERSION_GE(7000)
teller_set.insert("tile");
int8_teller_set.insert("tile");
teller_set.insert("flatten_contiguous_range");
int8_teller_set.insert("flatten_contiguous_range");
teller_set.insert("rnn");
int8_teller_set.insert("rnn");
teller_set.insert("fill_constant_batch_size_like");
int8_teller_set.insert("fill_constant_batch_size_like");
#endif
#if CUDA_VERSION >= 10020
teller_set.insert("reshape");
teller_set.insert("reshape2");
int8_teller_set.insert("reshape");
int8_teller_set.insert("reshape2");
#endif
#if IS_TRT_VERSION_GE(8000)
teller_set.insert("sparse_fc");
int8_teller_set.insert("sparse_fc");
teller_set.insert("sparse_multihead_matmul");
int8_teller_set.insert("sparse_multihead_matmul");
#endif
#if IS_TRT_VERSION_GE(8522)
teller_set.insert("flash_multihead_matmul");
int8_teller_set.insert("flash_multihead_matmul");
teller_set.insert("cross_multihead_matmul");
int8_teller_set.insert("cross_multihead_matmul");
teller_set.insert("qk_multihead_matmul");
int8_teller_set.insert("qk_multihead_matmul");
#endif
#if IS_TRT_VERSION_GE(8200)
teller_set.insert("round");
int8_teller_set.insert("round");
teller_set.insert("set_value");
teller_set.insert("index_select");
int8_teller_set.insert("index_select");
int8_teller_set.insert("einsum");
teller_set.insert("einsum");
#endif
}
bool operator()(const framework::OpDesc& desc,
bool use_no_calib_int8 = false,
bool with_dynamic_shape = false,
bool forbid_dynamic_op_enter_into_trt = false,
bool use_explicit_quantization = false) override {
const std::string op_type = desc.Type();
std::unordered_set<std::string> control_set = {"conditional_block",
"while"};
std::unordered_set<std::string> feed_fetch_set = {"feed", "fetch"};
if (control_set.find(op_type) != control_set.end()) {
return false;
}
if (feed_fetch_set.find(op_type) != feed_fetch_set.end()) {
return false;
}
if (forbid_dynamic_op_enter_into_trt && IsDynamicShapeOp(desc)) {
return false;
}
// do not support the op which is labeled the `skip_quant`
if ((desc.HasAttr("namescope") &&
PADDLE_GET_CONST(std::string, desc.GetAttr("op_namescope")) ==
"/skip_quant_2/") ||
desc.HasAttr("skip_quant"))
return false;
std::unordered_set<std::string> act_op_list = {
"relu", "relu6", "sigmoid",
"elu", "selu", "softsign",
"softplus", "stanh", "thresholded_relu",
"exp", "log", "sqrt",
"abs", "sin", "cos",
"tan", "tanh", "sinh",
"cosh", "asin", "acos",
"atan", "asinh", "acosh",
"atanh", "ceil", "celu",
"erf", "floor", "round",
"sign", "silu", "logical_not",
"reciprocal", "tanh_shrink", "logsigmoid",
"rsqrt", "swish", "hard_sigmoid",
"hard_swish", "leaky_relu"};
std::unordered_set<std::string> unary_list = {
"exp", "log", "sqrt", "abs", "sin",
"cos", "tan", "tanh", "sinh", "cosh",
"asin", "acos", "atan", "asinh", "acosh",
"atanh", "ceil", "celu", "floor", "round",
"sign", "logical_not", "reciprocal", "tanh_shrink", "logsigmoid",
"erf", "bitwise_not", "equal", "not_equal", "rsqrt"};
// Static shape does not support 0 or 1 dim's input.
if (!with_dynamic_shape) {
auto inputs = desc.Inputs();
for (auto iter : inputs) {
for (auto var_name : iter.second) {
auto* block = desc.Block();
if (block) {
auto* var_desc = block->FindVarRecursive(var_name);
// Can't get feed op's TensorDesc
if (op_type != "feed" && var_desc && !var_desc->Persistable()) {
const auto shape = var_desc->GetShape();
if (shape.size() == 1 || shape.empty()) return false;
}
}
}
}
}
if (act_op_list.find(op_type) != act_op_list.end()) {
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
#if !IS_TRT_VERSION_GE(7000)
if (op_type == "erf") {
VLOG(3) << op_type << " op does not support tensorrt.";
return false;
}
#endif
#if !IS_TRT_VERSION_GE(8600)
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVarRecursive(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (x_shape.empty() && unary_list.find(op_type) != unary_list.end()) {
VLOG(3) << op_type
<< " op does not support 0 dim input when TensorRT < 8.6.";
return false;
}
#endif
}
if (op_type == "dropout") {
/*
* Some OpDescs Attribute support both constant value and dynamic
* runtime value (which is a Variable(s) type). But TensorRT maybe
* only support constant value Attribute, so we shall distinguish
* this case in time and return False in OpTeller.Tell().
* If Attribute is Variable(s), HasAttr() will return False
*/
if (!desc.HasAttr("dropout_prob", /*with_attr_var=*/false)) {
VLOG(3)
<< "Skip to convert into TRT while found Attribute('dropout_prob') "
"is Variable type in dropout.";
return false;
}
}
if (op_type == "pool2d") {
// If Attribute is Variable(s), HasAttr() will return False
if (!desc.HasAttr("ksize", /*with_attr_var=*/false)) {
VLOG(3) << "Skip to convert into TRT while found Attribute('ksize') is "
"Variable type in pool2d.";
return false;
}
std::vector<int> paddings =
PADDLE_GET_CONST(std::vector<int>, desc.GetAttr("paddings"));
if (paddings.size() > 2) {
return false;
}
if (desc.Input("X").size() != 1) {
VLOG(3) << "TRT Pool2d expect 1 input, but got "
<< desc.Input("X").size();
return false;
}
if (desc.Output("Out").size() != 1) {
VLOG(3) << "TRT Pool2d has only 1 output, but got "
<< desc.Output("Out").size();
return false;
}
if (desc.HasAttr("data_format")) {
std::string data_format =
PADDLE_GET_CONST(std::string, desc.GetAttr("data_format"));
if (data_format == "NHWC" || data_format == "NDHWC") {
return false;
}
}
if (!desc.HasAttr("pooling_type")) {
return false;
} else {
std::string pool_type =
PADDLE_GET_CONST(std::string, desc.GetAttr("pooling_type"));
if (pool_type != "max" && pool_type != "avg") {
VLOG(3) << "Wrong pool op type, the trt do not support the "
<< pool_type << " pool type.";
return false;
}
if (pool_type == "avg") {
if (desc.HasAttr("global_pooling")) {
if (!PADDLE_GET_CONST(bool, desc.GetAttr("global_pooling"))) {
if (desc.HasAttr("exclusive")) {
if (PADDLE_GET_CONST(bool, desc.GetAttr("exclusive"))) {
std::vector<int> ksize =
PADDLE_GET_CONST(std::vector<int>, desc.GetAttr("ksize"));
for (size_t i = 0; i < ksize.size(); i++) {
if (ksize[i] <= paddings[i]) {
VLOG(3) << "the padding size should be less than the "
"filter size "
"for exclusive-counting pooling.";
return false;
}
}
}
}
}
}
}
}
}
if (op_type == "conv2d" || op_type == "conv2d_transpose" ||
op_type == "fused_conv2d_add_act" || op_type == "depthwise_conv2d" ||
op_type == "depthwise_conv2d_transpose") {
if (desc.Input("Input").size() != 1) {
VLOG(3) << "TRT Conv2d expect 1 input, but got "
<< desc.Input("Input").size() << " input.";
return false;
}
if (desc.Input("Filter").size() != 1) {
VLOG(3) << "TRT Conv2d expect 1 filter, but got "
<< desc.Input("Filter").size() << " filter.";
return false;
}
if (desc.HasAttr("enable_int8")) {
if (op_type == "conv2d" || op_type == "fused_conv2d_add_act") {
if (!desc.HasAttr("Input_scale")) {
VLOG(3) << "Input scale not found. TRT int8"
" requires conv/deconv to have "
"input quantization scales.";
return false;
}
}
}
if (op_type == "conv2d_transpose" ||
op_type == "depthwise_conv2d_transpose") {
if (!desc.HasAttr("dilations")) {
return false;
} else {
const std::vector<int> dilations =
PADDLE_GET_CONST(std::vector<int>, desc.GetAttr("dilations"));
if (dilations[0] != 1 || dilations[1] != 1) {
VLOG(3) << "In conv2d_transpose, Dilations must be (1, 1) for "
"tensorRT, but given ("
<< dilations[0] << ", " << dilations[1] << ")";
return false;
}
}
}
if (desc.Output("Output").size() != 1) {
VLOG(3) << "TRT Conv2d expect 1 output, but got "
<< desc.Output("Output").size() << " output.";
return false;
}
// strides > 1 and 'SAME' is only supported by trt7.0 above
#if !IS_TRT_VERSION_GE(7000)
if (op_type == "conv2d" || op_type == "fused_conv2d_add_act" ||
op_type == "depthwise_conv2d") {
if (desc.HasAttr("padding_algorithm") && with_dynamic_shape) {
auto padding_algorithm =
PADDLE_GET_CONST(std::string, desc.GetAttr("padding_algorithm"));
if (padding_algorithm == "SAME" && desc.HasAttr("strides")) {
const std::vector<int> strides =
PADDLE_GET_CONST(std::vector<int>, desc.GetAttr("strides"));
// there is no issue if strides.size() less than 2
if (strides.size() > 1) {
for (size_t i = 0; i < strides.size(); i++) {
if (strides[i] > 1) return false;
}
}
}
}
}
#endif
auto* block = desc.Block();
if (block) {
auto* filter_var_desc =
block->FindVarRecursive(desc.Input("Filter")[0]);
if (!filter_var_desc->Persistable()) {
#if IS_TRT_VERSION_GE(8600)
#else
LOG(INFO)
<< "Trt below 8.6 not support conv2d's filter is a intermedoate "
"tensor in conv2d op, please upgarde your TensorRT.";
return false;
#endif
}
}
}
if (op_type == "deformable_conv") {
if (!desc.HasAttr("groups") || !desc.HasAttr("strides") ||
!desc.HasAttr("paddings"))
return false;
auto* block = desc.Block();
auto input_name = desc.Input("Input")[0];
auto* input_desc = block->FindVarRecursive(input_name);
const auto input_shape = input_desc->GetShape();
if (input_shape.size() != 4) {
VLOG(3) << "Input of deformable conv should be 4-D Tensor, but got "
<< input_shape.size();
return false;
}
auto filter_name = desc.Input("Filter")[0];
auto* filter_desc = block->FindVarRecursive(filter_name);
const auto filter_shape = filter_desc->GetShape();
int groups = PADDLE_GET_CONST(int, desc.GetAttr("groups"));
if (input_shape[1] != filter_shape[1] * groups) {
VLOG(3) << "The number of input channels should be equal to filter "
<< "channels * groups. But got input channels "
<< input_shape[1] << "filter channels " << filter_shape[1];
return false;
}
const std::vector<int> strides =
PADDLE_GET_CONST(std::vector<int>, desc.GetAttr("strides"));
if (strides.size() != 2) {
VLOG(3) << "The size of strides should be 2, but got "
<< strides.size();
return false;
}
const std::vector<int> paddings =
PADDLE_GET_CONST(std::vector<int>, desc.GetAttr("paddings"));
if (paddings.size() != 2) {
VLOG(3) << "The size of paddings should be 2, but got "
<< paddings.size();
return false;
}
}
if (op_type == "bmm") {
if (!with_dynamic_shape) {
return false;
}
}
if (op_type == "range") {
if (!with_dynamic_shape) {
return false;
}
#if IS_TRT_VERSION_LT(8400)
auto* block = desc.Block();
auto start_var_name = desc.Input("Start")[0];
auto* start_var_desc = block->FindVarRecursive(start_var_name);
auto start_dtype = start_var_desc->GetDataType();
if (start_dtype == framework::proto::VarType::FP32 ||
start_dtype == framework::proto::VarType::FP64) {
return false;
}
#endif
}
if (op_type == "sign") {
#if IS_TRT_VERSION_GE(8200)
if (!with_dynamic_shape) {
return false;
}
#else
VLOG(3) << "sign op is only supported by trt8.2 above ";
return false;
#endif
}
if (op_type == "logical_not") {
#if IS_TRT_VERSION_GE(8400)
if (!with_dynamic_shape) {
return false;
}
#else
VLOG(3) << "logical_not op is only supported by trt8.4 above because of "
"cast op";
return false;
#endif
}
if (op_type == "softmax") {
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVarRecursive(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (with_dynamic_shape && (x_shape.size() == 1 || x_shape.empty())) {
int axis = desc.HasAttr("axis")
? PADDLE_GET_CONST(int, desc.GetAttr("axis"))
: -1;
if (axis > 0) {
return false;
}
}
}
if (op_type == "group_norm") {
if (!desc.HasAttr("epsilon") || !desc.HasAttr("groups") ||
!desc.HasAttr("data_layout"))
return false;
auto registry = GetPluginRegistry();
if (registry == nullptr) return false;
std::string layout_str =
PADDLE_GET_CONST(std::string, desc.GetAttr("data_layout"));
if (layout_str != "NCHW") {
VLOG(3) << "Group norm trt plugin only support NCHW layout, but got "
<< layout_str;
return false;
}
}
if (op_type == "concat") {
if (!desc.HasAttr("axis")) {
return false;
}
int axis = PADDLE_GET_CONST(int, desc.GetAttr("axis"));
if (!with_dynamic_shape) {
if (axis == 0) return false;
}
auto concat_inputs = desc.Inputs();
if (concat_inputs.find("AxisTensor") != concat_inputs.end()) {
if (!desc.Input("AxisTensor").empty()) {
return false;
}
}
}
if (op_type == "transpose2" || op_type == "transpose") {
if (!desc.HasAttr("axis")) {
return false;
}
std::vector<int> axis =
PADDLE_GET_CONST(std::vector<int>, desc.GetAttr("axis"));
if (!with_dynamic_shape && axis[0] != 0) return false;
if (axis.size() >= nvinfer1::Dims::MAX_DIMS) return false;
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVarRecursive(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (axis.size() != x_shape.size()) return false;
int dims = x_shape.size();
std::vector<int> perm(nvinfer1::Dims::MAX_DIMS);
for (int i = 0; i < dims; i++) {
perm[i] = axis[i];
}
auto is_valid_permutation = [&](int dims,
const std::vector<int>& permutation) {
std::bitset<nvinfer1::Dims::MAX_DIMS> found;
for (int i = 0; i < dims; ++i) {
const int x = permutation[i];
if ((x < 0) || (x >= dims) || found[x])
return false; // Out of bounds or duplicate
found.set(x);
}
return true;
};
if (!is_valid_permutation(dims, perm)) {
VLOG(3) << "Invalid permutation dimensions for trt transpose op "
"converter: duplicate or out of bound.";
return false;
}
}
if (op_type == "flatten2" || op_type == "flatten") {
if (!desc.HasAttr("axis")) {
return false;
} else {
#if IS_TRT_VERSION_GE(7130)
#else
if (with_dynamic_shape) return false;
#endif
int axis = PADDLE_GET_CONST(int, desc.GetAttr("axis"));
if (axis != 1) return false;
}
}
if (op_type == "flatten_contiguous_range") {
if (!with_dynamic_shape) {
if (!desc.HasAttr("start_axis") || !desc.HasAttr("stop_axis")) {
return false;
}
int start_axis = PADDLE_GET_CONST(int, desc.GetAttr("start_axis"));
int stop_axis = PADDLE_GET_CONST(int, desc.GetAttr("stop_axis"));
auto x_var_name = desc.Input("X")[0];
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
auto* x_var_desc = block->FindVarRecursive(x_var_name);
const auto x_shape = x_var_desc->GetShape();
int dims = x_shape.size();
if (dims == 0) {
VLOG(3) << op_type
<< " op does not support input's dim is 0 in tensorrt "
"static shape mode.";
return false;
}
if (start_axis < 0) start_axis += dims;
if (start_axis == 0) {
VLOG(3) << "TRT flatten_contiguous_range not support the "
"batch-dimension being changed";
return false;
}
if (stop_axis < 0) stop_axis += dims;
for (int i = start_axis; i <= stop_axis; ++i) {
if (x_shape[i] < 0) {
VLOG(3) << "On TRT static shape,flatten_contiguous_range input dim "
"should be > 0";
return false;
}
}
}
}
if (op_type == "gather") {
auto gather_inputs = desc.Inputs();
if (gather_inputs.find("Axis") != gather_inputs.end()) {
if (!desc.Input("Axis").empty()) {
return false;
}
}
if (!with_dynamic_shape) {
return false;
} else {
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
#if !IS_TRT_VERSION_GE(7000)
auto* x_var_desc = block->FindVarRecursive(desc.Input("X")[0]);
const auto x_shape = x_var_desc->GetShape();
if (x_shape.size() == 1) {
VLOG(3) << "Gather does not support 1-dimensional input in tensorrt";
return false;
}
#endif
}
}
if (op_type == "gather_nd") {
if (!with_dynamic_shape) return false;
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
#if IS_TRT_VERSION_LT(8200)
auto index_var_name = desc.Input("Index")[0];
auto* index_var_desc = block->FindVarRecursive(index_var_name);
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVarRecursive(x_var_name);
const auto index_shape = index_var_desc->GetShape();
const auto x_shape = x_var_desc->GetShape();
if (x_shape.size() <= 2) {
VLOG(3) << "gather_nd op requires the input's dimension to be greater "
"than 2";
return false;
}
if (x_shape.size() != index_shape.size()) {
VLOG(3) << "gather_nd op Index input dims size [" << index_shape.size()
<< " ] not equal to x dims size [" << x_shape.size() << "]";
return false;
}
#endif
}
if (op_type == "index_select") {
#if !IS_TRT_VERSION_GE(8200)
return false;
#endif
auto gather_inputs = desc.Inputs();
if (!with_dynamic_shape) {
return false;
} else {
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
auto index_var_name = desc.Input("Index")[0];
auto* index_var_desc = block->FindVarRecursive(index_var_name);
// The index input must be int32 or int64 datatype.
if (index_var_desc->GetDataType() !=
paddle::framework::proto::VarType_Type::VarType_Type_INT32 &&
index_var_desc->GetDataType() !=
paddle::framework::proto::VarType_Type::VarType_Type_INT64) {
VLOG(3)
<< "Index select op Index input data type must be int32 or int64";
return false;
}
}
}
if (op_type == "take_along_axis") {
#if IS_TRT_VERSION_GE(8200)
if (!with_dynamic_shape) return false;
auto* block = desc.Block();
auto input_var_name = desc.Input("Input")[0];
auto index_var_name = desc.Input("Index")[0];
auto* input_var_desc = block->FindVarRecursive(input_var_name);
auto* index_var_desc = block->FindVarRecursive(index_var_name);
const auto input_shape = input_var_desc->GetShape();
const auto index_shape = index_var_desc->GetShape();
if (input_shape.size() != index_shape.size()) {
VLOG(3) << "take_along_axis op Index input dims size ["
<< index_shape.size() << " ] not equal to input dims size ["
<< input_shape.size() << "]";
return false;
}
#else
VLOG(3) << "take_along_axis op is only supported by trt8.2 above ";
return false;
#endif
}
if (op_type == "anchor_generator") {
if (!with_dynamic_shape) return false;
}
if (op_type == "yolo_box") {
return false;
}
if (op_type == "yolo_box_head") {
return false;
}
if (op_type == "arg_max" || op_type == "arg_min") {
if (!desc.HasAttr("axis", /*with_attr_var=*/false)) {
VLOG(3) << "Skip to convert into TRT while found Attribute('axis') is "
"Variable type in arg_max.";
return false;
}
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVarRecursive(x_var_name);
auto x_dtype = x_var_desc->GetDataType();
if (!(x_dtype == framework::proto::VarType::FP32 ||
x_dtype == framework::proto::VarType::FP16 ||
x_dtype == framework::proto::VarType::FP64)) {
return false;
}
int axis = desc.HasAttr("axis")
? PADDLE_GET_CONST(int64_t, desc.GetAttr("axis"))
: -1;
bool flatten = desc.HasAttr("flatten")
? PADDLE_GET_CONST(bool, desc.GetAttr("flatten"))
: false;
int dtype = desc.HasAttr("dtype")
? PADDLE_GET_CONST(int, desc.GetAttr("dtype"))
: 3;
if (axis == 0 || flatten || (dtype != 2 && dtype != 3)) return false;
}
if (op_type == "affine_channel") {
if (!desc.HasAttr("data_layout")) return false;
auto data_layout = common::StringToDataLayout(
PADDLE_GET_CONST(std::string, desc.GetAttr("data_layout")));
if (data_layout != phi::DataLayout::kNCHW) return false;
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
auto x_var_name = desc.Input("X")[0];
auto* x_var_desc = block->FindVarRecursive(x_var_name);
const auto x_shape = x_var_desc->GetShape();
if (x_shape.size() == 2) {
return false;
}
}
if (op_type == "multiclass_nms" || op_type == "multiclass_nms3") {
auto* block = desc.Block();
if (block == nullptr) {
VLOG(3) << "The block desc is nullptr, we can't continue to analyze. "
"Developers need to check whether block_desc is passed in "
"the pass.";
return false;
}
auto multiclass_nms_inputs = desc.Inputs();
if (multiclass_nms_inputs.find("RoisNum") !=
multiclass_nms_inputs.end()) {
if (!desc.Input("RoisNum").empty()) {
return false;
}
}
for (auto& param_name : multiclass_nms_inputs) {
for (auto& var_name : param_name.second) {
auto* var_desc = block->FindVarRecursive(var_name);
const auto shape = var_desc->GetShape();
if (shape.size() != 3) {
VLOG(3) << "multiclass_nms op dims != 3 not supported in tensorrt, "
"but got dims "
<< shape.size() << ", so jump it.";
return false;
}
}
}
bool has_attrs =
(desc.HasAttr("background_label") &&
desc.HasAttr("score_threshold") && desc.HasAttr("nms_top_k") &&
desc.HasAttr("keep_top_k") && desc.HasAttr("normalized"));
if (has_attrs == false) return false;
// TODO(wangxinxin08): tricky solution because the outputs of batchedNMS
// plugin are not constient with those of multiclass_nms3
if (desc.HasAttr("nms_eta") == false) return false;
auto nms_eta = PADDLE_GET_CONST(float, desc.GetAttr("nms_eta"));
if (nms_eta <= 1.0) return false;
auto nms_top_k = PADDLE_GET_CONST(int, desc.GetAttr("nms_top_k"));
if (nms_top_k < 0) return false;
auto keep_top_k = PADDLE_GET_CONST(int, desc.GetAttr("keep_top_k"));
if (keep_top_k < 0) return false;
auto registry = GetPluginRegistry();
if (registry == nullptr) return false;
}
if (op_type == "nearest_interp") {
std::vector<std::string> attrs{
"interp_method", "align_corners", "scale", "out_h", "out_w"};
for (auto const& attr : attrs) {
if (!desc.HasAttr(attr)) return false;
}
if (desc.HasAttr("data_layout")) {
auto data_layout = common::StringToDataLayout(
PADDLE_GET_CONST(std::string, desc.GetAttr("data_layout")));
if (data_layout != phi::DataLayout::kNCHW &&
data_layout != phi::DataLayout::kNHWC)
return false;
}
auto interp_method =
PADDLE_GET_CONST(std::string, desc.GetAttr("interp_method"));
if (interp_method != "nearest") return false;
auto scale = PADDLE_GET_CONST(float, desc.GetAttr("scale"));
auto out_h = PADDLE_GET_CONST(int, desc.GetAttr("out_h"));
auto out_w = PADDLE_GET_CONST(int, desc.GetAttr("out_w"));
auto align_corners =
PADDLE_GET_CONST(bool, desc.GetAttr("align_corners"));
if (!(scale > 0.f && (out_h <= 0 && out_w <= 0))) {
if (out_h <= 0) {
VLOG(3) << "out_h must be greater than 0 if scale is not set.";
return false;
}
if (out_w <= 0) {
VLOG(3) << "out_w must be greater than 0 if scale is not set.";
return false;
}
}
if ((scale <= 0.f) && with_dynamic_shape) {
VLOG(3) << "dynamic shape not support scale not set.";
return false;
}
// When align_corners = true, the paddle's and trt_layer's results has
// diff
if (align_corners && scale != 1) {
return false;
}
}
if (op_type == "nearest_interp_v2") {
std::vector<std::string> attrs{"data_layout",
"interp_method",
"align_corners",
"scale",
"out_h",
"out_w"};
for (auto const& attr : attrs) {
if (!desc.HasAttr(attr)) return false;
}
auto data_layout = common::StringToDataLayout(
PADDLE_GET_CONST(std::string, desc.GetAttr("data_layout")));
if (data_layout != phi::DataLayout::kNCHW &&
data_layout != phi::DataLayout::kNHWC)
return false;
auto interp_method =
PADDLE_GET_CONST(std::string, desc.GetAttr("interp_method"));
if (interp_method != "nearest") return false;
#if IS_TRT_VERSION_GE(8200)
auto resize_inputs = desc.Inputs();
if (with_dynamic_shape &&
resize_inputs.find("SizeTensor") != resize_inputs.end() &&
desc.Input("SizeTensor").size() == 2) {
return true;
}
#endif
auto scale = PADDLE_GET_CONST(std::vector<float>, desc.GetAttr("scale"));
auto out_h = PADDLE_GET_CONST(int, desc.GetAttr("out_h"));
auto out_w = PADDLE_GET_CONST(int, desc.GetAttr("out_w"));
if (!(out_h > 0 && out_w > 0)) {
if (scale.size() < 2) return false;
if (scale[0] <= 0.f || scale[1] <= 0.f) {
VLOG(3) << "scale factor must be greater than 0 if out_h or out_w is "
"not set.";
return false;
}
}
}
if (op_type == "bilinear_interp_v2") {
// trt 7011 result in test_solov2_trt_fp32.py TRT fp32 diff
#if IS_TRT_VERSION_LT(7100)
return false;
#endif
std::vector<std::string> attrs{"data_layout",
"interp_method",
"align_corners",
"scale",
"out_h",
"out_w"};
for (auto const& attr : attrs) {
if (!desc.HasAttr(attr)) {
VLOG(3) << "The op_type " << op_type << " doesn't have the attr "
<< attr << " and return false";
return false;
}
}
auto resize_inputs = desc.Inputs();
if (resize_inputs.find("SizeTensor") != resize_inputs.end()) {
#if IS_TRT_VERSION_GE(8200)
if (desc.Input("SizeTensor").size() == 2) {
return true;
}
#else
if (!desc.Input("SizeTensor").empty()) {
VLOG(3)
<< "The Paddle-TRT doesn't support the SizeTensor for op_type "
<< op_type;
return false;
}
#endif
}
if (resize_inputs.find("OutSize") != resize_inputs.end()) {
if (!with_dynamic_shape) {
VLOG(3) << "Static shape don't support the OutSize for op_type "
<< op_type;
return false;
}
}
auto data_layout = common::StringToDataLayout(
PADDLE_GET_CONST(std::string, desc.GetAttr("data_layout")));
if (data_layout != phi::DataLayout::kNCHW &&
data_layout != phi::DataLayout::kNHWC) {
VLOG(3) << "The op_type " << op_type
<< " is not NCHW or NHWC return false";
return false;
}
auto interp_method =
PADDLE_GET_CONST(std::string, desc.GetAttr("interp_method"));
if (interp_method != "bilinear") {
VLOG(3) << "The interp_method of op_type " << op_type
<< " is not bilinear";
return false;
}
bool has_scale_input_size =
(resize_inputs.find("Scale") != resize_inputs.end());
if (!has_scale_input_size ||
(has_scale_input_size && desc.Input("Scale").size() != 1)) {
const std::vector<float> scale =
PADDLE_GET_CONST(std::vector<float>, desc.GetAttr("scale"));
if (scale.size() <= 1) {
if (!desc.HasAttr("out_h") || !desc.HasAttr("out_w")) {
VLOG(3) << "The op_type " << op_type
<< " doesn't have Scale and the scale size <=1 and without "
"out_h / out_w, it will return false";
return false;
}
auto out_h = PADDLE_GET_CONST(int, desc.GetAttr("out_h"));